India Develops First Indigenous COVID-19 Antibody Detection Kit, to Play Critical Role in Surveillance of Infection

In what could play a critical role in surveillance for coronavirus infection, the National Institute of Virology in Pune has successfully developed the first indigenous antibody detection kit for COVID-19. Notably, the testing kits are cost-effective, sensitive, rapid, and a large number of samples can be tested at any level of clinical setting, public health centers and hospitals.

Developed in a month’s time, the testing kit would help to study the presence of anti-SARS-CoV-2 IgG antibodies in the Indian population. 

The test kit has the advantage of testing 90 samples together in a single run of 2.5 hours, so that healthcare professionals can proceed quickly with necessary next steps, Union Health Minister Harsh Vardhan said on Sunday. Also Read - 'Stringent Action, Less Relaxation', Harsh Vardhan Counters CM Kejriwal's 'Time to Reopen Capital' Remark

“National Institute of Virology, Pune, has successfully developed the 1st indigenous anti-SARS-CoV-2 human IgG ELISA test kit for antibody detection of COVID-19,” the minister said in one of a series of tweets.

“This robust test will play a critical role in surveillance of proportion of population exposed to SARSCoV2 infection,” he said.

This kit was validated at two sites in Mumbai and has high sensitivity and accuracy, Vardhan said.
The ELISA-based testing is easily possible even at district level, he said.

The Indian Council of Medical Research-National Institute of Virology, Pune, technology has been transferred to Zydus Cadila for mass-scale production.

The Drug Controller General has granted commercial production and marketing permission to Zydus, Vardhan said.

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Dogs can sniff out cancer in blood with 97% accuracy

Dogs have smell receptors 10,000 times more accurate than humans', making them highly sensitive to odours we can not perceive.

"Although there is no cure for cancer, early detection offers the best hope," said lead researcher. "A highly sensitive test for detecting cancer could save thousands of lives and change the way the disease is treated," he said.

For the study, the team used a form of clicker training to teach four beagles to distinguish between normal blood serum and samples from patients with malignant lung cancer.

Although one beagle -- aptly named Snuggles -- was unmotivated to perform, the other three correctly identified lung cancer samples 96.7 per cent times and normal samples 97.5 per cent times.

"This work is very exciting because it paves the way for further research along two paths, both of which could lead to new cancer-detection tools," said the researcher.

"One is using canine scent detection as a screening method for cancers, and the other would be to determine the biologic compounds the dogs detect and then design cancer-screening tests based on those compounds," he said.

The team plans to use canine scent detection to develop a non-invasive way of screening for cancer and other life-threatening diseases.

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A Tech giant is using retinal images to assess cardiovascular risk factors

A tech giant has said that it has been trying to use deep learning techniques on one of its computer vision engines to try and assess cardiovascular risk factors, with heart attacks and strokes continuing to be among the top public health issues.

Computer vision tasks include methods for acquiring, processing, analysing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information.

"Using deep learning algorithms trained on data from 284,335 patients, we were able to predict CV [cardiovascular] risk factors from retinal images with surprisingly high accuracy for patients from two independent datasets of 12,026 and 999 patients,"said a researcher.

Doctors usually take into account various risk factors for patient assessment such as some genetic data (like age and sex) and data related to lifestyle components (such as a smoking and blood pressure).

While most of these factors can be ascertained by simply asking the patient, other factors such as cholesterol require drawing blood. Other disease factors such as diabetes significantly increases risk.
According to the researcher, the computer vision algorithm can distinguish between the retinal images of a smoker from that of a non-smoker at least seven out of 10 times.

In addition, while doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, the algorithm could go further to predict the systolic blood pressure within 11 mmHg on average for patients overall, including those with and without high blood pressure.  

"Recently, we’ve seen many examples of how deep learning techniques can help to increase the accuracy of diagnoses for medical imaging, especially for diabetic eye disease," the researcher explained. "In addition to detecting eye disease, images of the eye can very accurately predict other indicators of CV health. This discovery is particularly exciting because it suggests we might discover even more ways to diagnose health issues from retinal images."

The tech giant said that its algorithm used the entire image to quantify the association between the image and the risk of heart attack or stroke. 

"Given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70% of the time. This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol," the researcher said.

The researcher also said that the tech giant was able to understand how the algorithm was making its prediction. According to her, the operational methodology of the algorithm can in future allow the tech giant to generate a heatmap that shows which pixels were the most important elements for a predicting a specific CV risk factor. 

For example, the algorithm paid more attention to blood vessels for making predictions about blood pressure, the researcher said. She added that explaining how the algorithm is making its prediction gives the doctor more confidence in the algorithm itself. 

The search giant said that it is looking forward to developing and testing the algorithm on larger and more comprehensive datasets to make it more useful for patients and doctors. 

"To make this useful for patients, we will be seeking to understand the effects of interventions such as lifestyle changes or medications on our risk predictions and we will be generating new hypotheses and theories to test," the researcher said.

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